Beyond Impostor Syndrome: Building Workforce Confidence Through AI and Coaching

Article Summary

Impostor phenomenon is usually treated as a private struggle—something capable people manage quietly and alone. This guest lecture for the Center for Business Research and Development at De La Salle University argues that framing is both inaccurate and expensive. When experienced professionals hesitate to decide, delay asking for help, or stay silent in the rooms where judgment matters most, the cost is not personal. It is organizational: slower adoption, weaker decision quality, thinner leadership pipelines, and attrition among exactly the people an institution can least afford to lose.

The session reframes confidence as an organizational capability—shaped by feedback environments, access to coaching, learning design, and psychological safety—rather than as a fixed individual trait. That reframing carries direct implications for how organizations adopt artificial intelligence. Organizations rarely struggle with AI because the tools are unavailable; they struggle because workers are unclear about expectations, uncertain about their own competence, and unsupported when judgment is required. Confidence therefore operates as an adoption variable, influencing whether people use new systems well, avoid them quietly, or over-delegate judgment to them.

Drawing on more than twenty years in health system operations and on enterprise workforce transformation at a 68,000-caregiver integrated health system—alongside in-progress doctoral research examining impostor phenomenon and AI-enabled coaching among healthcare leaders—the session examines coaching and skills intelligence as practical mechanisms for building confidence at scale, and the conditions under which each succeeds or fails.

The session opens with Becoming the Leader, an eight-minute documentary following healthcare leaders as they navigate self-doubt, growth, and responsibility, and is structured to leave substantial time for discussion. Observations drawn from applied practice are distinguished throughout from formal research findings, which remain in progress.

Practical Takeaways

1. Treat confidence as an organizational property—shaped by feedback environments, coaching access, learning design, and psychological safety—not a fixed individual trait.
2. Recognize confidence as an AI adoption variable: unclear expectations and uncertain competence lead people to avoid new systems quietly or over-delegate judgment to them.
3. Reexamine what institutions measure, and where they may be mismeasuring, when confidence is understood as a property of the system rather than the individual.
4. Use AI-enabled coaching and skills intelligence to extend access to development—while guarding against widening the gap for those who lack mentorship.
5. Distinguish applied observations from formal research findings, and treat worker confidence as a variable in AI adoption outcomes rather than a wellbeing footnote.

Questions Posed for Discussion

1. If confidence is an organizational property rather than an individual one, what should institutions be measuring, and what are they currently mismeasuring?
2. Where does AI-enabled coaching extend access to development, and where does it risk widening the gap between those who already have mentorship and those who do not?
3. What would a research agenda look like that treats worker confidence as a variable in AI adoption outcomes rather than as a wellbeing footnote?

Related Insights

• Beyond Impostor Syndrome: Building Workforce Confidence Through AI and Coaching Shared focus: impostor syndrome, psychological safety, and AI-enabled coaching for workforce confidence.
• Leadership Confidence in Global Health Workforce Development Shared focus: leadership confidence and AI-enabled learning across the global health workforce.

Related Event

Visit De La Salle University for details on the Center for Business Research and Development.

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